Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation.

BACKGROUND

Spinal cord stimulation (SCS) effectively reduces opioid usage in some patients, but preoperatively, there is no objective measure to predict who will most benefit.

OBJECTIVE

To predict successful reduction or stabilization of opioid usage after SCS using machine learning models we developed and to assess if deep learning provides a significant benefit over logistic regression (LR).

METHODS

We used the IBM MarketScan national databases to identify patients undergoing SCS from 2010 to 2015. Our models predict surgical success as defined by opioid dose stability or reduction 1 year after SCS. We incorporated 30 predictors, primarily regarding medication patterns and comorbidities. Two machine learning algorithms were applied: LR with recursive feature elimination and deep neural networks (DNNs). To compare model performances, we used nested 5-fold cross-validation to calculate area under the receiver operating characteristic curve (AUROC).

RESULTS

The final cohort included 7022 patients, of whom 66.9% had successful surgery. Our 5-variable LR performed comparably with the full 30-variable version (AUROC difference <0.01). The DNN and 5-variable LR models demonstrated similar AUROCs of 0.740 (95% CI, 0.727-0.753) and 0.737 (95% CI, 0.728-0.746) ( P = .25), respectively. The simplified model can be accessed at SurgicalML.com .

CONCLUSION

We present the first machine learning-based models for predicting reduction or stabilization of opioid usage after SCS. The DNN and 5-variable LR models demonstrated comparable performances, with the latter revealing significant associations with patients' pre-SCS pharmacologic patterns. This simplified, interpretable LR model may augment patient and surgeon decision making regarding SCS.